1 option
From Static Ground to Moving Ground Wind Tunnels: Feature Selection for Reliable Estimation of Drag Coefficient Discrepancies The Ohio State University
- Format:
- Book
- Conference/Event
- Author/Creator:
- Singh, Yuvraj, author.
- Jayakumar, Adithya, author.
- Rizzoni, Giorgio, author.
- Conference Name:
- WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)
- Language:
- English
- Subjects (All):
- Wind tunnel tests.
- Mathematical models.
- Drag.
- Spoilers.
- Aerodynamics.
- Certification.
- Measurements.
- Simulation and modeling.
- Local Subjects:
- Wind tunnel tests.
- Mathematical models.
- Drag.
- Spoilers.
- Aerodynamics.
- Certification.
- Measurements.
- Simulation and modeling.
- Physical Description:
- 1 online resource
- Place of Publication:
- Warrendale, PA SAE International 2026
- Summary:
- Moving ground wind tunnels offer a more accurate test environment for ground vehicle drag coefficient measurement due to their highly realistic representation of the boundary layer phenomenon. However, historically most vehicles have been tested on static ground wind tunnels. As a result, the measured drag coefficient of these vehicles may not be sufficiently realistic for certification purposes. Therefore, it is valuable to build statistical models to estimate moving ground wind tunnel drag coefficient by using information from a static ground wind tunnel and other relevant vehicle characteristics such as presence of aerodynamic devices (spoilers, air dams, et cetera). However, to build accurate statistical models, appropriate predictive features must be identified as a first step. In this paper, an aerodynamic feature selection study has been conducted to identify vehicle characteristics that contribute to drag coefficient estimation discrepancies between a static- and a moving ground wind tunnel. Aerodynamic datasets generally consist of several non-gaussian continuous variables as well as discrete variables, which may be mutually dependent on each other. Appropriate feature selection metrics have been identified using a data simulation approach previously published by the authors. The paper concludes by providing an overview of potential techniques for model development using the selected features
- Notes:
- Vendor supplied data
- Access Restriction:
- Restricted for use by site license
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.